Measuring the reorganization of national and local economies in response to natural and other shocks is an enduring challenge. From a complex adaptive systems (CAS) perspective, the economy is never in equilibrium but instead constantly adjusts structurally and functionally to shocks. The firms or agents comprising an economy may manage to reorganize their interactions during a shock in such a way as not only to mitigate its impact but also to enter onto a new subsequent growth pattern that is more favorable in terms of longer-term growth.

An economy’s new growth pattern following a shock is the result of interactions among firms and industries that reflect coping with or adapting to the new economic environment; this is known as an emergent property. Inter-industry job flows have widely been used as one indicator for measuring interactions among industries. For approximating local-level job-flows, a ‘stepping down’ method from national to local-levels is used in cases where inter-industry job-flow data are available only at the national-level. However, agents in different industries are likely to respond in different ways to shocks. In other words, different local industries likely adapt their interactions in unique ways, and will not simply follow the national-level average inter-industry job-flow patterns.

In this paper, we develop and apply distinct county-level inter-industry job transitions measures. An economic shock causes workers to be redistributed across industries as they expand or contract. The net difference or change in the distribution of employment across industries over time captures the inter-industry job-flows in the period, where employment in each industry is a component of a j-dimensional vector (j is the number of industries). Recognizing this, we apply a simple vector operation, cosine similarity, to estimate the difference between two vectors in any given county over time. This allows us to identify how much inter-industry labor reallocation or reorganization occurred in each county, or to what extent the local economy was reorganized.

In what follows we first describe the county-level employment data used and our method for calculating reorganization in local economies. Then we develop a simple econometric growth model to examine the effect of reorganization on each county’s employment growth rate before and after the recession. Our measure is statistically significant in a regression analysis with changes in pre- and post-recession growth rates as the dependent variable. Our results contribute to understanding the effects of changing industry structure and labor market flexibility on economic growth.